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QUiLT (Quantitative Ultrasound in Longitudinal Tissue Tracking): Stitching 2D images into 3D Volumes for Organ Health Monitoring
Volumetric property maps of organs (e.g., stiffness) are clinically significant and valuable in diagnosing the onset and monitoring the progression of a large multitude of diseases such as autosomal dominant polycystic kidney disease (ADPKD) and chronic liver disease (CLD). Unlike 2D property maps, 3D property maps allow for precise, consistent, and accurate longitudinal comparison because they eliminate the variabilities associated with the underlying image acquisition protocol. 3D organ reconstructions provide a holistic view of the organ and serve as a foundation for generating 3D property maps. Existing methods for reconstructing 3D images of organs include MRI and CT. Ultrasound emerges as a viable alternative due to its low cost, portability, and ability for repeated use.
This thesis presents a workflow for generating 3D images of the kidney and liver from 2D ultrasound images. We augment a 1D ultrasound probe with a set of sensors that allow for its localization in 3D space. The pose information and 2D images are combined to generate accurate organ volumes, specifically for the kidney and liver. In ex vivo studies, our method reconstructs renal volumes with an accuracy that is comparable to the current clinical gold-standard, CT. The liver, however, poses additional challenges due to its size and location underneath the ribcage – multiple partial scans are required to capture the entire organ. These scans need to be co-registered to generate a complete volume. Unfortunately, the liver parenchyma lacks features, such as edges and corners, that are used by conventional image registration techniques. To circumvent this, we propose a set of quantitative features based on the liver vasculature and develop an algorithm to determine their correspondences in 3D space. The features serve as internal landmarks that are used to perform deformable registration on the partial scans to create a whole-organ view. We validate the proposed approach with simulated and in vivo data; the method consistently performs as well or better than existing registration methods. Our method aims to provide an easy and cost-effective way for clinicians to monitor organ property changes over time, thereby paving the way for early diagnosis and prevention of diseases.
Thesis Supervisor:
Brian W. Anthony, PhD
Associate Principal Research Scientist, MIT IMES; Principal Research Scientist, MIT Mechanical Engineering;
Director, Master of Engineering in Manufacturing Program, MIT; Co-Director, Medical Electronic Device Realization Center, IMES
Thesis Committee Chair:
Justin Solomon, PhD
Associate Professor; Principal investigator, Geometric Data Processing Group, MIT Department of Electrical Engineering & Computer Science, Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT Center for Computational Science and Engineering (CCSE)
Thesis Readers:
Polina Golland, PhD
Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science; Principal Investigator, Computer Science and Artificial Intelligence Laboratory, MIT
Alex Goehler, MD, PhD
Precision Medicine and AI Lead, Novartis Institutes for Biomedical Research (NIBR)
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Zoom invitation –
Melinda Chen is inviting you to a scheduled Zoom meeting.
Topic: Melinda Chen MEMP PhD Thesis Defense
Time: Thursday, September 15, 2022 10:00 AM Eastern Time (US and Canada)
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